Comparison of algorithms for various SNR.

<p>FACTID (col. 1) and FACT (col. 2) results are shown (“noiseless” model at top, with realistic, noisy copies from DW simulations of decreasing SNR). Coronal projections are shown of tracts intersecting two mediofrontal ROIs (shown in upper left; AND logic). The number of increasing dissimilarities with decreasing SNR is apparent, with related Dice and eta² coefficients plotting in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043415#pone-0043415-g006" target="_blank">Fig. 6</a>. Coloration by orientation of medial voxel of each fiber, with (x, y, z) associated with (red, green, blue). Also shown are RK4 results (distinct coloration), with several changes in tract results apparent.</p

Comparison of FACTID (left column) and FACT (right column) algorithms for various spatial resolutions.

<p>Coronal projections are shown of tracts intersecting two mediofrontal ROIs (shown in upper left; AND logic). Dissimilarities in tract bundle location from the top panel are shown for results of 2.5 and 3 mm resolution with dotted (missing) lines. Coloration by orientation of medial voxel of each fiber, with (x, y, z) associated with (red, green, blue). Also shown are RK4 results (distinct coloration), with several differences in tract bundle results apparent.</p

Algorithm comparison for rotation test.

<p>FACTID (col. 1) and FACT (col. 2) results are given for various rotations of coordinate axes. Corono-axial projections are shown of tracts intersecting a single ROI in the posterior corpus callosum which intersects the cingulum (shown in upper left). Dissimilarities in tract bundle location from average results are shown with dotted (missing) and dashed (additional) lines. Coloration by FA magnitude of each voxel, ranging from 0.2 (yellow) to 1.0 (red). Also shown for comparison are results for the tracts through the same ROI using dtiStudio-FACT (col. 3) and DTI-Query RK4 (col. 4), with distinct colorations from separate software. The former yields quite similar results to those of FACT in Column 2, and RK4 shows changes in fiber structure (though not with missing bundles).</p

Test metrics for the phantom results for the representative longest tracts per ROI (shown in Fig. 8), and also including the best values for each ROI (as generally each ROI contained more than one tract) in brackets.

<p>The bottom row gives the mean value per column. NB: lower values per test reflect better values <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043415#pone.0043415-Poupon1" target="_blank">[32]</a>.</p

Schematic of tractography algorithms.

<p>Panel A shows an example of the propagation of four test tracks in 2D FACT algorithm (bold arrows show orientations of primary eigenvectors), where tracks cross from edge to edge of the lower left voxel and continue to facewise neighbors. Panel B shows the same system in the FACTID approach, where tracts propagate to the octagon surfaces in each voxel (bold line, gray area), allowing diagonal motion. The analogous 3D regions are shown for FACT and FACTID in panels C and D, respectively. Panel E illustrates the simple steps in the FACTID algorithm to allow for diagonal tract propagation (see text for details).</p

Algorithm comparison for various N_ave. FACTID (col. 1) and FACT (col. 2) results are shown for various SNR (determined by N_ave).

<p>Coronal projections are shown of tracts intersecting two mediofrontal ROIs (shown in upper left; AND logic). Dissimilarities in tract bundle location from the top panel are shown for N_ave<16 results with dotted (missing) and dashed (additional) lines. Related Dice and eta² coefficients are plotted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043415#pone-0043415-g004" target="_blank">Fig. 4</a>. Coloration by orientation of medial voxel of each fiber, with (x, y, z) associated with (red, green, blue). Also shown are RK4 results (distinct coloration), with fiber differences in inferior- and anterior-running tracts appearing.</p

Table_1_Frequency-Dependent Effects of Cerebellar Repetitive Transcranial Magnetic Stimulation on Visuomotor Accuracy.docx

The cerebellum plays a critical role in acquiring visuomotor skills. Visuomotor task mastery requires improving both visuomotor accuracy and stability; however, the cerebellum’s contribution to these processes remains unclear. We hypothesized that repetitive transcranial magnetic stimulation (rTMS) of the cerebellum exerts frequency-dependent modulatory effects on both accuracy and stability in subjects performing a visuomotor coordination task (i.e., pursuit rotor task). We recruited 43 healthy volunteers and randomly assigned them to the high-frequency (HF), low-frequency (LF), and sham rTMS groups. We calculated changes in performance of the pursuit rotor task at the highest rotation speed and the minimum distance from target as indices of accuracy. We also calculated the intertrial variability (standard deviations) of time on target and distance from target as indices of stability. Visuomotor accuracy was significantly enhanced in the HF group and disrupted in the LF group compared to the sham group, indicating frequency-dependent effects of rTMS. In contrast, both HF and LF rTMS demonstrated no significant change in visuomotor stability. Surprisingly, our findings demonstrated that the accuracy and stability of visuomotor performance may be differentially influenced by cerebellar rTMS. This suggests that visuomotor accuracy and stability have different underlying neural mechanisms and revealed the possibility of training strategies based on cerebellar neuromodulation.</p

Similarity indices for various N_ave. <i>C</i>_D, <i>C</i>_Dw and eta² (formulations given in text) of N_ave<16 results compared with the highest SNR, N_ave = 16 case, for FACT and FACTID for tracts of interest (shown in <b>Fig. 3</b>).

<p>Similarity indices for various N_ave. <i>C</i>_D, <i>C</i>_Dw and eta² (formulations given in text) of N_ave<16 results compared with the highest SNR, N_ave = 16 case, for FACT and FACTID for tracts of interest (shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043415#pone-0043415-g003" target="_blank"><b>Fig. 3</b></a>).</p

Comparisons of phantom test tractography results.

<p>16 “underlying truth” tracts of the phantom are shown in (A), reproduced from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043415#pone-0043415-g004" target="_blank">Fig. 4C</a> of Fillard et al. (2011) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043415#pone.0043415-Poupon1" target="_blank">[32]</a>. Tractography results from this study, represented by the longest tract passing through each test ROI (pink squares, numbered), are shown for FACT in (B) and for FACTID in (C). NB: identifying tract colors are the same in B and C, but different than in A. Quantitative scores per ROI are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043415#pone-0043415-t001" target="_blank">Table 1</a>.</p

Data_Sheet_2_The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan.xlsx

IntroductionThe hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive.MethodsThis study utilized resting-state functional MRI data from 350 healthy adults (aged 20–85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan.ResultsThe principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal–parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect.DiscussionOur study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes.</p